Current models predict that up to 60% of species are at a greater risk of extinction due to climate change. This increased risk is largely due to the expected poleward shift in climate conditions at the rate of 0.08 to 1.26 kilometers per year. Most models currently used to predict species responses to climate change rely on unrealistic dispersal assumptions and statistical relationships between species presence/absence with environmental factors instead of its driving demographic processes. With improved access to datasets and more robust modeling techniques, we examined what types of plant species may be able to track environmental shifts. We generated virtual plant species using a Bayesian multivariate statistical model incorporating dispersal, demography, and functional traits, and an analytical integrodifference model that includes realistic dispersal kernels and demographic information to inform the potential for each species to follow climatic shifts based on their unique characteristics. While ecological data has become more available, virtual species are essential to this approach, as these data still tend to be sparse. From these models, a rate of spread based on dispersal and demography for each species can be generated and compared to the expected velocity of climate change globally and for each biome.
Results/Conclusions
Preliminary results indicate that approximately 80% of plant species will be unable to track their current habitat conditions based on the global velocity of climate change, meaning that a large majority of species will be required to adapt to their new local habitats in order to persist without intervention. Estimating extinction risk using virtual species sampled from biome-specific trait values and estimates of climate change velocities for each biome reduces the estimated percentage of species at risk of extinction. This approach provides an estimate of the proportion of species and predictions of the types of species (based on functional traits such as seed dispersal distance) that could go extinct as a result of their inability to track their environments, allowing responsible parties to prioritize efforts to anticipate and mitigate the potentially disastrous consequences of large scale extinction. These results indicate that species across taxa are likely to require mitigation efforts for long-term persistence.